Determining a health condition of a structure

ABSTRACT

The disclosure relates to structural health monitoring (SHM). In particular determining a health condition of a structure, such as a bridge, based on vibration data measured of the bridge. Measured vibration data is calibrated (410-450). Features are then extracted from the calibrated data (610-630) and a support vector machine classifier is then applied (720) to the extracted features to determine (730) the health condition of a part of the structure. Training of the support vector machine classifier by a machine learning process (910) is also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from Australian ProvisionalPatent Application No 2013901528 filed on 1 May 2013, the content ofwhich is incorporated herein by reference.

TECHNICAL FIELD

The present invention generally relates to structural health monitoring(SHM). Aspects of the invention include computer-implemented methods,software and systems for determining a health condition of a structure,such as a bridge, based on vibration data of the bridge.

BACKGROUND

A structure, such as a bridge, building or a tower, normally consists ofmultiple structural components. These structural components areconnected typically by mechanical means to provide workability of thestructure. In this disclosure, the connection between structuralcomponents (typically joints) or the structural components themselvesare referred to as a part of the structure. A part of the structure maydeteriorate with use of the structure over time, which degradesintegrity of the structure and may cause economic loss or personalinjuries. Therefore, the health condition of the structure is monitoredconstantly or regularly in many ways to avoid undesired loss orinjuries.

Any discussion of documents, acts, materials, devices, articles or thelike which has been included in the present specification is not to betaken as an admission that any or all of these matters form part of theprior art base or were common general knowledge in the field relevant tothe present disclosure as it existed before the priority date of eachclaim of this application.

SUMMARY

In a first aspect of the invention there is provided acomputer-implemented method for determining a health condition of a partof a structure, comprising:

-   -   receiving or accessing vibration data of the part of the bridge,        the vibration data being measured by at least one accelerometer;    -   extracting a feature of the vibration data based on frequency        analysis of the vibration data; and    -   determining the health condition of the part of the bridge by a        support vector machine classifier based on the feature of the        vibration data.

It is an advantage that the invention applies the support vector machineclassifier to determine the health condition of the part of thestructure based on the feature of the vibration data. The support vectormachine classifier may be determined by a learning process based onhistory vibration data across one or more parts of one or morestructures. As a result, a physical model for the structure, which isusually complex and unknown, is not required for the invention todetermine the health condition of one or more parts of the structure.

The structure may be a bridge and the part of the bridge may be a jointof the bridge.

The vibration data may comprise data in rest and data in movement,wherein the data in rest represents acceleration of the part of thebridge substantially caused by ambient factors, and the data in movementrepresents acceleration of the part of the structure substantiallycaused by an external vibration source.

The ambient factors may include wind.

The external vibration source may comprise a vehicle moving on thestructure.

The method may further comprise calibrating coordinate axis directionsof the at least one accelerometer based on the vibration data of thepart of the structure by principal component analysis to calibrate thevibration data.

It is an advantage that calibration of the coordinate axis direction ofthe at least one accelerometer makes coordinate systems of the at leastone accelerometer consistent and comparable.

Calibration of the coordinate axis directions of the at least oneaccelerometer may comprise transformation of the coordinate system ofthe at least one accelerometer.

Calibration of the coordinate axis directions of the at least oneaccelerometer may comprise aligning −Z axis direction of the at leastone accelerometer with gravity direction based on the data in rest.

Calibration of the coordinate axis directions of the at least oneaccelerometer may comprise aligning X axis direction of the at least oneaccelerometer with a direction along which the external vibration sourcemoves based on the data in movement.

The frequency analysis may comprise fast Fourier transform of thevibration data.

The feature of the vibration data may comprise amplitude spectrums ofthe fast Fourier transform of the vibration data.

The support vector machine classifier may comprise a classificationfunction g taking a form of g(E)=wE−b, where E represents the feature ofthe vibration data, w and b are parameters determined by a machinelearning process.

The machine learning process may comprise a supervised machine learningprocess.

The machine learning process may comprise an unsupervised machinelearning process.

The vibration data may be stored in a datastore and the determinedhealth score may be stored in the same or different datastore.

In a second aspect of the invention there is provided a computersoftware program, including machine-readable instructions, when executedby a processor, causes the processor to perform the method describedabove.

In a third aspect of the invention there is provided a computer systemfor determining a health condition of a part of a structure, the systemcomprising a processor that is adapted to:

-   -   receive or access vibration data of the part of the bridge, the        vibration data being measured by at least one accelerometer;    -   extract a feature of the vibration data based on frequency        analysis of the vibration data; and    -   determine the health condition of the part of the structure by a        support vector machine classifier based on the feature of the        vibration data.

The processor may be further adapted to calibrate coordinate axisdirection of the at least one accelerometer based on the vibration dataof the part of the bridge by principal component analysis to calibratethe vibration data.

In a fourth aspect of the invention there is provided a system fordetermining a health condition of a part of a structure, the systemcomprising:

-   -   a data acquisition unit, configured to receive or access        vibration data of the part of the bridge, the vibration data        being measured by at least one accelerometer;    -   a feature extraction unit connected to the data acquisition        unit, the feature extraction unit being configured to extract a        feature of the vibration data based on frequency analysis of the        vibration data; and    -   a health condition determination unit connected to the feature        extraction unit, the health condition determination unit being        configured to determine the health condition of the part of the        structure by a support vector machine classifier based on the        feature of the vibration data.

The system may further comprise a data calibration unit connected withthe data acquisition unit and the feature extraction unit, the datacalibration unit being configured to calibrate coordinate axis directionof the at least one accelerometer based on the vibration data of thepart of the bridge by principal component analysis to calibrate thevibration data.

In a fifth aspect of the invention there is provided acomputer-implemented method for training a support vector machineclassifier for use in determining a health condition of h part of astructure, the method comprising:

-   -   receiving or accessing vibration data of one or more parts of        one or more structures measured by one or more accelerometers;    -   extracting two or more features of the vibration data based on        frequency analysis of the vibration data; and    -   applying a machine learning process to determine the support        vector machine classifier based on the two or more features of        the vibration data.

In a sixth aspect of the invention there is a provided a computersoftware program, including machine-readable instructions, when executedby a processor, causes the processor to perform the method describeddirectly above.

In a seventh aspect of the invention there is provided a system fortraining a support vector machine classifier for use in determining ahealth condition of a part of a structure, the system comprising:

-   -   a data acquisition unit, configured to receive or access        vibration data of one or more parts of one or more structures        measured by one or more accelerometers;    -   a feature extraction unit connected to the data acquisition        unit, the feature extraction unit being configured to extract        two or more features of the vibration data by frequency analysis        of the vibration data; and    -   a support vector machine trainer unit connected to the feature        extraction unit, the support vector machine trainer unit being        configured to apply a machine learning process to determine the        support vector machine classifier based on the two or more        features of the vibration data.

Optional features of a single aspect of the invention described here arealso optional features of the other aspects described here whereappropriate.

BRIEF DESCRIPTION OF THE DRAWINGS

At least one example of the invention will be described with referenceto the accompanying drawings, in which:

FIG. 1 shows a general structure of a bridge to be monitored for ahealth condition;

FIG. 2 is an illustrative diagram of vibration data measured by anaccelerometer;

FIG. 3 is a diagram of a health condition detector for detection of ahealth condition of a bridge according to an embodiment;

FIG. 4 is a flow chart of calibrating vibration data according to anembodiment;

FIG. 5A-5D are illustrative diagrams to show transformation ofcoordinate system of the at least one accelerometer according to anembodiment;

FIG. 6 is a flow chart of extracting a feature vector of vibration dataaccording to an embodiment;

FIG. 7 is a flow chart of classifying a feature vector of vibration dataaccording to an embodiment;

FIG. 8 is a diagram showing experimental results of determining a healthcondition of a bridge according to an embodiment of the presentinvention; and

FIG. 9 is a flow chart of training a support vector machine classifieraccording to an embodiment.

It should be noted that the same numeral represents the same or similarelements throughout the drawings.

BEST MODES OF THE INVENTION

Classic vibration theories for SHM have been developed to describecharacteristics of vibration of structures. However, exact physicalmodels of the structures are usually complex and unknown. Operationaland environmental variations also have significant impacts on thestructures. In order to tackle these uncertainties on structures, theinvention of this disclosure treats the SHM problem as a patternrecognition problem. Specifically, with utilization of historical bridgevibration data, machine learning techniques are utilized to train aclassifier to detect failure patterns. The present disclosure primarilycomprises (1) data calibration; (2) feature extraction; and (3) supportvector machine classifier.

Examples of a structure that this disclosure relates to includesbridges, towers and other general buildings. As shown in FIG. 1, thestructure of this example is a bridge 100 that is monitored for a healthcondition.

In FIG. 1, the bridge typically consists of multiple components,particularly, a bridge deck, pillars 1 and 2, arches 1, 2 and 3 (arches1 and 3 are partially shown in FIG. 1).

Those components are connected together typically by mechanical means toprovide workability of the bridge. As a result, a joint is formedbetween two or more components of the bridge. For example, joint 1(surrounded by a dashed box in FIG. 1) is formed between the arches 1, 2and the pillar 1; similarly, joint 2 is formed between the arches 2, 3and the pillar 2, etc. In this example, a components and a joint of abridge are collectively referred to as a part of a bridge. Moreover, inpractice, the structure of the bridge may be different from thestructure shown in FIG. 1 without departing from the spirit and scope ofthe invention as broadly described.

To determine the health condition of the bridge, accelerometers areinstalled at the joints. In FIG. 1, accelerometer array 1 consisting ofthree accelerometers are installed to measure vibration datarepresenting vibration events occurring at the joint 1, andaccelerometer array 2 consisting of three accelerometers are installedto measure vibration data representing vibration events occurring at thejoint 2. Although three accelerometers are used for each joint in theexample shown in FIG. 1, more or less accelerometers can be used whereappropriate.

The vibration events are normally caused by external vibration sourcesfor example vehicular traffic on the bridge. Particularly, when thevehicle in FIG. 1 is moving over the joint 1 on the bridge, the vehiclenormally causes the joint 1 to vibrate for a period of time, which inturn causes changes to acceleration of the joint 1. Therefore, in thisexample, vibration data for vibration events of the joints 1 and 2 arerepresented by acceleration data measured at accelerometer arrays 1 and2, respectively.

In practice, the accelerometer arrays constantly or regularly measurethe acceleration data of their respective joints. As a result, theacceleration data measured by the accelerometer arrays include (1) datain rest, which are measured when the external vibration source haslittle effect on the corresponding accelerometer array, for example whenthe vehicle is at position 1 in FIG. 1, acceleration of the joint 1 issubstantially caused by ambient factors such as wind instead of thevehicle and (2) data in movement, which are measured when the externalvibration source has substantial effect on the correspondingaccelerometer array, or the vibration event actually happens, forexample when the vehicle is moving at position 2 in FIG. 1, accelerationof the joint 1 is substantially caused by the vehicle instead of theambient factors.

FIG. 2 is an illustrative diagram of vibration data for a vibrationevent measured by an accelerometer typically a 3-axis accelerometer.

The accelerometer normally measures acceleration data in 3 directionsbased on its 3-D coordinate system consisting of X, Y and Z axes.Therefore, an output of the accelerometer can be considered as a 3-Dvector with X, Y and Z components.

In order to measure the vibration data for a vibration event withrespect to the joints 1 and 2, each accelerometer in the respectiveaccelerometer array measures acceleration amplitude for all 3 axes at afrequency of 400 Hz for 1.5 seconds. As a result, outputs of eachaccelerometer in respective accelerometer array form 600 samples of thevibration event with each sample being represented by a 3-D vector. X, Yand Z components of the samples are shown in FIG. 2.

In this embodiment, vibration data of the X, Y, and Z components for thefirst 100 samples have smaller vibration amplitude, and the vibrationamplitude abruptly change from the 101^(st) sample, which means avibration event actually starts from the 101^(st) sample, as shown inFIG. 2. In other words, the vibration data for the first 100 samples isdata in rest, and the vibration data for the rest of the samples is datain movement. The vibration data may be arranged in other ways withoutdeparting from the spirit and scope of the present invention as broadlydescribed.

FIG. 3 is a diagram of a health condition detector 310 for determining ahealth condition of a bridge according to an embodiment of the presentinvention. The health condition detector 310 includes an input port 320,a data acquisition unit 330, a data calibration unit 340, a featureextraction unit 350, a health condition determination unit 360, asupport vector classifier trainer unit 380 and an output port 370. Thehealth condition detector 310 determines the health condition of thebridge based on vibration data of the bridge without establishing aphysical model of the bridge. In this example, the health conditiondetector 310 is also be used to train the support vector machineclassifier used to determine the health condition of the bridge.

It should be noted that the units in the health condition detector 310can be connected directly or indirectly, physically or logically.

The links between the ports and units in the health condition detector310 may be physical links or logical links or their combinations whereappropriate.

Input Port

The input port 320 is an interface with a data source from whichvibration data for vibration events is obtained. The data source may bethe accelerometer arrays 1 and 2 when the health condition detector 310is used in real-time. Alternatively, the data source may be a datastore(not shown in FIG. 3) that stores the vibration data measured by theaccelerometer arrays.

Data Acquisition Unit

The data acquisition unit 330 receives the vibration data for thevibration events from the data source via the input port 320;alternatively, the data acquisition unit 330 may access the vibrationdata at the data source.

Generally speaking, when the accelerometers are installed at the joints1 and 2, the orientations of their respective coordinate systems aredifferent from each other. As a result, it is impractical to compare andprocess the vibration data from different accelerometers. Althoughphysical or manual calibration of accelerometers can solve this problem,this usually causes extra efforts and costs. Therefore, in thisembodiment, before the vibration data from each accelerometer is furtherprocessed, the vibration data is sent to the data calibration unit 340to calibrate their respective coordinate systems automatically.

Data Calibration Unit

The data calibration unit 340 is now described with reference to FIGS. 4and 5, which calibrates, i.e., mathematically transforms, the coordinatesystems of the accelerometers to make them consistent and comparable.

Upon receipt of the vibration data for the vibration events at the datacalibration unit 340 from the data acquisition unit 330, the datacalibration unit 340 separates 410 the data in rest from the data inmovement in the vibration data for the vibration events.

The calibration is performed by two steps: (1) Z axis alignment,aligning −Z direction with gravity; (2) XY axes alignments, onhorizontal plane, the direction with maximum variance is selected as newX axis, and new Y axis is determined by the right hand rule accordingly.With such calibration or transformation, the new coordinate systems ofthe accelerometers are consistent, comparable and comply with right handcoordinate system.

Then data calibration unit 340 then calculates 420 an accelerationvector in rest Ar, which is a 3-D vector <x_(Ar),y_(Ar),z_(Ar)>,representing the acceleration of an accelerometer when no vibrationevent occurs at the joint, in this case, the acceleration of theaccelerometer is substantially caused by the ambient factors. Theacceleration vector in rest Ar is obtained by averaging the first 100samples' vibration magnitudes (at all the 3 axes) of all the vibrationevents with respect to an accelerometer. Thus, direction of Ar isconsistent with direction of gravity in the accelerometer's coordinatesystem, as shown in FIG. 5A.

The first step of the calibration is to make the new—Z axis of thecoordinate system of the accelerometer point to the gravity direction orthe direction of Ar. It is achieved by rotating the original coordinatesystem twice. The first rotation is performed along the original X axison the original YZ plane. The angle between—Z and Ar's projection onplane YZ, denoted as θ_(ArZ) ^(YZ), is calculated by:

$\theta_{ArZ}^{YZ} = {\cos^{- 1}{\left\{ \frac{\left( {y_{Ar},z_{Ar}} \right)\left( {0,{- 1}} \right)}{{{y_{Ar},z_{Ar}}}_{2}} \right\}.}}$

The first rotation matrix can be obtained accordingly:

$M_{ArZ}^{YZ} = {\begin{bmatrix}{\cos\;\theta_{ArZ}^{YZ}} & {\sin\;\theta_{ArZ}^{YZ}} \\{{- \sin}\;\theta_{ArZ}^{YZ}} & {\cos\;\theta_{ArZ}^{YZ}}\end{bmatrix}.}$

After the first rotation, X axis remains unchanged. Y and Z axes arechanged. Thus, Ar's second and third components are updated by:

$\begin{bmatrix}y_{Ar}^{\prime} \\z_{Ar}^{\prime}\end{bmatrix} = {M_{ArZ}^{YZ}\begin{bmatrix}y_{Ar} \\z_{Ar}\end{bmatrix}}$

The second rotation is performed along current Y axis on the current XZplane. The angle between current −Z axis and current Ar on the currentXZ plane, denoted as θ_(ArZ) ^(XZ), is calculated by:

$\theta_{ArZ}^{XZ} = {\cos^{- 1}\left\{ \frac{\left( {x_{Ar},z_{Ar}^{\prime}} \right)\left( {0,{- 1}} \right)}{{{x_{Ar},z_{Ar}^{\prime}}}_{2}} \right\}}$

The second rotation matrix is obtained by:

$M_{ArZ}^{XZ} = \begin{bmatrix}{\cos\;\theta_{ArZ}^{XZ}} & {\sin\;\theta_{ArZ}^{XZ}} \\{{- \sin}\;\theta_{ArZ}^{XZ}} & {\cos\;\theta_{ArZ}^{XZ}}\end{bmatrix}$

After the second rotation, Y axis remains unchanged. X and Z axes arechanged. As a result of the above two rotations, −Z axis is aligned 430with Ar, i.e. the gravity direction, as shown in FIG. 5B.

The second step of calibration is to determine directions of X and Yaxes. X and Y axes represent orthogonal directions in horizontal plane.The absolute values of the coordinates in FIGS. 5C and 5D representamplitudes. The sign represents direction. Particularly, a positivevalue means the acceleration has the same direction as the axis, and anegative value means the acceleration has the opposite direction. Zeromeans there is no acceleration. Acceleration is caused by a force and italso represents movement in terms of both direction and distance. Highacceleration implies long distance movement. Therefore, the X/Ycoordinate system can reflect the movements of an accelerometer and abridge joint to which the accelerometer is applied. The arrow “d” inFIGS. 5C and 5D represents the direction on which the majority of thevibration events happen. The assumption is that the majority vibrationevents are caused by the vehicle moving on the bridge. Thus, “d” alsoindicates the vehicle moving direction, as shown by the arrow above thevehicle in FIG. 1. In the second step, the data in rest, which is thefirst 100 samples in the vibration data for each vibration event, areignored, and only the data in movement is considered as the data in restdo not represent acceleration data when the vibration event actuallyhappens.

The data in movement for each vibration event is projected on thehorizontal plane, as shown in FIG. 5C. Each dot in FIG. 5C represents aprojection of each sample in the data in movement for each vibrationevent measured by an accelerometer. The arrow d in FIG. 5C shows thedirection on which the most vibrations happen.

The desired direction represented by d is the direction with the maximumvariance of the dots' locations. With u=(x_(u),y_(u)) representing a doton the horizontal plane, these dots' variance projected on d can becomputed by:

${\frac{1}{N}{\sum\limits_{n = 1}^{N}\;\left\{ {{d^{T}u_{n}} - {d^{T}\overset{\_}{u}}} \right\}^{2}}} = {d^{T}{Sd}}$

where N denotes the number of dots,

$\overset{\_}{u} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}\; u_{n}}}$and S is the covariance matrix of u. Thus, d can be obtained by solvingthe following optimization problem:

$\max\limits_{d}{d^{T}{Sd}}$ subject  to:d^(T)d = 1

Lagrange multiplier can be applied to convert the constrainedmaximization problem into an unconstrained maximization problem:d ^(T) Sd+λ(1−d ^(T) d)

The solution can be obtained by making the derivative equal to zero,which leads to:Sd=λd.

It can be seen from the above equation that d and λ are eigenvector andeigenvalue for S, respectively. Since d^(T)Sd=λ, d will be the directionwith maximum variance when λ is the largest eigenvalue.

Once d is determined, the angle between the original X axis and d can bedetermined. The coordinate system can be rotated accordingly to make thenew X axis aligned 440 with d, as shown in the FIG. 5D.

After the new Z and X axes are determined, the new Y axis is aligned 450by right hand rule, as known to a person skilled in the art.

The above process is repeated by the data calibration unit 340 for eachaccelerometer to calibrate the coordinate axis directions of allaccelerometers, the vibration data including the data in rest and thedata in movement for the vibration events are represented by the new andconsistent coordinate system across each accelerometer. Then thevibration data is sent to the feature extraction unit 350 for featureextraction.

Feature Extraction Unit

The feature extraction unit 350 will now be described with reference toFIG. 6.

The feature extraction unit 350 identifies the samples that comprise avibration event received from the data acquisition unit 330, or the datacalibration unit 340 if the coordinate systems of the accelerometers arecalibrated.

Then fast Fourier transform (FFT) is performed 610 on the time domainvibration data for the vibration event. It is an advantage that the useof frequency analysis avoids the issues with time domain features, sincetime domain vibration signals vary from vehicle to vehiclesignificantly. Both weight and speed impacts on the time domainvibration signal, for example, vibration magnitude peaks and can appearat different times and is difficult to calibrate. In the frequencydomain, vibration energy is measured by frequency spectrum which iscomparable and invariant to time shift allowing for the discovery ofsubtle patterns.

Take the components on X axis as an example, the X axis components ofthe vibration data for the vibration event is converted from time domain(x_(j)) into frequency domain A(f_(x)) by:

${{A\left( f_{x} \right)} = {\sum\limits_{j = 1}^{600}\;{x_{j}\omega_{600}^{{({j - 1})}{({f_{x} - 1})}}}}},$

where

${\omega_{600} = e^{\frac{{- 2}\;\pi\; i}{600}}},$f_(x) represents frequency, and x_(j) represents one of the 600 timedomain samples on X axis.

Then the absolute value |A(f_(x))| is computed 620 to represent anamplitude spectrum of the FFT.

Amplitude spectrums across X, Y and Z axes of all accelerometers appliedto a joint are then concatenated to construct 630 a vector E, which is afeature vector of the vibration data for the vibration event. It shouldbe noted that in the example shown in FIG. 1 as three accelerometers areapplied to each joint, the feature vector E is a 9-D vector.

The feature vector is then sent to the health condition determinationunit 360 to determine the health condition of the joint of bridge and/orthe support vector classifier trainer unit 380 to train the classifierto detect unhealthy patterns.

Health Condition Determination Unit

The health condition determination unit 360 will now be described withreference to FIG. 7.

Upon receipt of the feature vector E from the feature extraction unit350, the health condition determination unit 360 loads 710 a supportvector machine classifier, for example from the support vector trainingunit 380 (described below). The support vector machine classifier has aclassification function g taking a form of g(E)=wE−b, where E representsthe feature vector of the vibration data, w and b are parametersdetermined by a machine learning process.

Then the support vector machine classifier is applied 720 to the featurevector E to obtain an outcome of the support vector machine classifier,which is considered as a health score to measure the health condition ofthe joint of the bridge.

The health score can be any suitable measure that shows the healthaccording to some known scale. For example a health score in thisexample is a signed real value. A negative value means that the featurevector is associated with a faulty joint, and a positive value meansthat the feature vector is associated with a normal joint. The absolutevalue of the health score indicates a confidence level ofclassification. For instance, if the health value is −20, it means thatit is determined that the joint is a faulty joint with a confidencelevel of 20.

A health score can be determined for multiple feature vectors Eextracted from vibration data from the same joint of the bridge. Anoverall health score for that part of the bridge can be determined for aspecific period of time such as a week. For example, the health scoresdetermined for each vibration event of that part of the bridge and inturn feature vector E can be averaged.

Then the health score for that part of the bridge is sent to a recipientfor example the operator of the bridge via the output port 370 forfurther decision making. The health score in this example is stored instorage (not shown). The health scores can then be automaticallyanalysed, such as producing graphs and a statistical report of thehealth scores of one or more parts of the bridge over time. The reportmay indicate whether a threshold indicative of poor health of one ormore parts of a bridge is indicated so as to prioritise manualinspection and restorative action.

Features vectors E extracted from vibration events from a different partof the bridge can be used to determine a separate health score for thatdifferent part of the bridge.

Training of the SVM Classifier

The support vector machine trainer unit 380 will now be described withreference to FIG. 9.

As described above, the parameters w and b of the classificationfunction are determined by a machine learning process 910 performed inthis example by the support vector classifier training unit 380. Thismachine learning process 910 is based on history vibration data acrossone or more parts of the bridge that may include neighbour parts. Thehistory vibration data is in the form of feature vectors E determined bythe feature extraction unit 350. The support vector machine classiertraining unit 380 then makes the determined support vector machineclassifier available to the health condition determination unit 360 forapplication.

The support vector machine classifier training unit 380 may perform themachine learning process at intervals to update the support vectormachine classier and typically the most up to date version of thesupport vector machine classifier is received and applied by the healthcondition determination unit 360. It is an advantage of this examplethat the support vector classifier is continuously improved byincrementally learning from new incoming vibration data.

In one example, the support vector machine classifier is improved bytraining with vibration data calibrated to manual inspection scores soas to better account for interference. That is, some features extractedfrom the vibration data that are provided as input to the support vectormachine classifier trainer unit 380 may be labelled manually with ahealth score as determined manually by visual inspection of the relevantpart of the bridge. In this way the support vector machine classifiertrainer unit 380 continues to improve the support vector classifier.

Further the use of features extracted from vibration data from one ormore other structures or other parts of the same structure means thattransfer learning is also achieved by the support vector classifiertraining unit 380.

The determined support vector classifier can be provided to the outputport 370 where it can be stored in a datastore, such as computer memory.The support vector machine classifier can then be retrieved from memory,such as by a different health condition detector 310 for application.

In this specification, the machine learning 910 is supervised learningand/or unsupervised learning processes that train the SVM classifier soas to determine the parameters w and b, as described below.

Supervised Learning

The health condition of a part of a bridge such as a joint is describedby its vibration data for a vibration event caused by a moving vehicle.And the vibration data is characterised by the amplitude spectrums ofits FFT, namely the feature vector E as described above.

Supervised learning is performed on E to train a support vector machineclassifier, with history vibration data to differentiate faulty bridgeparts from normal ones.

The history vibration data for faulty and normal parts is grouped aspositive and negative class, respectively, to train the classifier. Andthe obtained classifier is then applied to new vibration data todetermine whether the new vibration data represents a normal joint or afaulty one.

From the history vibration data, feature vectors E and their labels canbe determined. Specifically, E∈R^(c) represents the vibration amplitudespectrums for vibration events, as described above, where c indicatesthe number of different frequencies. The label of the feature vector isrepresented by y∈{−1,1}, where y=−1 means that E is extracted fromhistory vibration data for a faulty part and y=1 otherwise. Theclassification model is a classification function g: R^(c)→{−1,1}. Theclassification function takes the form of g(E)=wE−b, where w and b arethe parameters of the model, which are determined from the supervisedlearning process.

Given a set of n training samples, i.e., history feature vectors andtheir labels, {(E_(i),y_(i))}_(i=1) ^(n), the learning process is todetermine the model parameters w and b by making sure that theclassification error of the obtained model g(E)=wE−b on the trainingsamples {(E_(i),y_(i))}_(i=1) ^(n) is minimized. Mathematically, thelearning process is equivalent to the following minimization problem:

${\min\limits_{w,\xi,b}{\frac{1}{2}{w}^{2}}} + {C{\sum\limits_{i = 1}^{n}\;\xi_{i}}}$s.t.  y_(i)(w E_(i) − b) ≥ 1 − ξ_(i), ξ_(i) ≥ 0, i ∈ [1, n],

where ξ_(i) is the slack variable which controls how much classificationerror is allowed for the training sample E_(i). The slack variable isintroduced into the training objective function because the trainingdata might not be separable sometimes and the slack variable allows usto find a relatively accurate hyper plane to split training data interms of their labels,

C is the variable to control the balance between

$\frac{1}{2}{w}^{2}\mspace{14mu}{and}\mspace{14mu}{\sum\limits_{i = 1}^{n}\;{\xi_{i}.}}$The former can be understood as the complexity of the mode, and thelater represents classification error. The learning objective is tominimize both classification error and model complexity. Thus, Ccontrols the influences of the two factors on the learning objective.

This minimization problem can be solved by Lagrangian multiplier andquadratic programming. Once the classification function g(E)=wE−b isdetermined, a health score for a new feature vector, denoted as E_(new),can be generated as g(E_(new))=wE_(new)−b. Then an overall health scorefor a part of a bridge in a specific time period can be obtained by theaveraging health scores over all the vibration events measured.

Unsupervised Learning

One-class support vector machine is an unsupervised kernel-basedapproach for anomaly detection. It assumes that all positive trainingsamples share some common properties to form one class. And negativetraining samples can have very different properties without anycommonness. It fits health condition determination in structural healthmonitoring, since there may exist many failure patterns and one-classSVM can cover all of them as outliers. Using unsupervised learning nolabelled training data is needed so that means no model of the structureis needed and no prior knowledge is needed of whether a part of thestructure is damaged in order to use the respective vibration data. As aresult using unsupervised learning the resulting classifier can accountfor unknown failure patterns.

Follow the settings of supervised learning, the unsupervised learningprocess can be regarded as the following optimization problem:

${\min\limits_{w,\xi,h}{\frac{1}{2}{w}^{2}}} + {\frac{1}{vl}{\sum\limits_{i = 1}^{n}\;\xi_{i}}}$s.t.  w E_(i) ≥ b − ξ_(i), ξ_(i) ≥ 0, i ∈ [1, n],

where l represents the number of training samples;

-   -   ν has the similar function as C, which controls the balance        between model complexity

$\frac{1}{2}{w}^{2}$and classification error

$\sum\limits_{i = 1}^{n}\;{\xi_{i}.}$

It is worth noting that the training samples {E_(i)}_(i=1) ^(n) onlycontain feature vectors and no label information is provided. Theoptimization problem can be solved by Lagrangian multiplier andquadratic programming. Once the classification function is obtained, ahealth score is generated in a same way as the supervised learning.

Experimental Results

This invention is tested on 6 bridge joints including one faulty joint.Around 3000 vibration events are measured for each joint. 10-foldcross-validation is performed for evaluating the invention. Experimentresults show that the method can achieve more than 99% and 85% ofclassification accuracy for supervised and unsupervised learning,respectively, as shown in FIG. 8.

It will be appreciated by persons skilled in the art that numerousvariations and/or modifications may be made to the invention as shown inthe specific embodiments without departing from the spirit and scope ofthe invention as broadly described.

For example, the part the health condition of which is to be determinedmay be the joint between the bridge deck and the pillar 1 or 2.

In another example, the number of the accelerometers in an accelerometerarray that is used to measure vibration data for vibration events may bejust one, which leads to a simpler 3-D feature vector instead of the 9-Dfeature vector in the embodiment described above.

Although the health condition detector 310 is shown as an independententity in FIG. 3, it may also be a logical or physical part of acomputing device for example a desktop, a laptop, a handheld device.

In another embodiment, the health condition detector 310 may beimplemented as a computer program including instructions performed by aprocessor(s) of a computing device to detect the health condition of abridge.

In a further embodiment, the ports and units in the health conditiondetector 310 may be distributed across multiple entities to performtheir respective functions.

Further, the application of the present invention is not intended to belimited to structural health condition determination; it may also beapplied to bridge traffic condition analysis based on bridge jointvibrations.

The health condition detector 310 in this example performs bothfunctions of determining the health of part of the bridge and trains thesupport vector machine classifier. In other examples, these twofunctions could be performed by separate devices. In this case, in eachentity only the units needed to perform the separate functions need beincluded. For example, a device that determines the health condition ofpart of the bridge need not have the support vector classifier trainerunit 380. Further, a device that trains the support vector machineclassifier need not have the health condition determine unit 360. If itis preferred not to process the vibration data twice, the dataacquisition unit 330, data calibration unit 340 and the featureextraction unit 350 could be included in only a first entity. Thefeatures extracted by the feature extraction unit 350 are then receivedby the other entity or accessed from datastore where the first entitycaused them to be stored.

It should be understood that the techniques of the present disclosuremight be implemented using a variety of technologies. For example, themethods described herein may be implemented by a series of computerexecutable instructions residing on a suitable computer readable medium.Suitable data store is computer readable media that can include volatile(e.g. RAM) and/or non-volatile (e.g. ROM, disk) memory, carrier wavesand transmission media. Exemplary carrier waves may take the form ofelectrical, electromagnetic or optical signals conveying digital datasteams along a local network or a publically accessible network such asthe internet.

It should also be understood that, unless specifically stated otherwiseas apparent from the following discussion, it is appreciated thatthroughout the description, discussions utilizing terms such as“receiving” or “sending” or “obtaining” or “calculating” or “storing” or“determining” or the like, refer to the action and processes of acomputer system, or similar electronic computing device, that processesand transforms data represented as physical (electronic) quantitieswithin the computer system's registers and memories into other datasimilarly represented as physical quantities within the computer systemmemories or registers or other such information storage, transmission ordisplay devices.

The invention claimed is:
 1. A computer-implemented method, comprising:monitoring a building structure by constantly or regularly performingthe steps of: receiving or accessing vibration data of a part of thestructure, the vibration data being measured by two or moreaccelerometers and, the vibration data representing acceleration of thepart of the structure substantially caused by an external vibrationsource; transforming the vibration data by aligning an axis direction ofthe vibration data from each of the two or more accelerometers with adirection corresponding to maximum variance of the vibration data tocreate calibrated vibration data; extracting an amplitude for afrequency of the calibrated vibration data based on frequency analysisof the calibrated vibration data; and determining a health condition ofthe part of the structure by a support vector machine classifier basedon the amplitude for the frequency.
 2. The method according to claim 1,wherein the vibration data comprises data in rest and data in movement,wherein the data in rest represents acceleration of the part of thestructure substantially caused by ambient factors, and the data inmovement represents the acceleration of the part of the structuresubstantially caused by an external vibration source.
 3. The methodaccording to claim 2, wherein the method further comprises: calibratingcoordinate axis directions of the two or more accelerometers based onthe vibration data of the part of the structure by principal componentanalysis to calibrate the vibration data.
 4. The method according toclaim 3, wherein calibrating coordinate axis directions of the two ormore accelerometers comprises aligning an axis direction of the at leastone accelerometer with a direction along which the external vibrationsource moves based on the data in movement.
 5. The method according toclaim 1, wherein the vibration data is stored in a datastore and thedetermined health condition is stored in the same or differentdatastore.
 6. The method according to claim 1, wherein the frequencyanalysis comprises fast Fourier transform of the vibration data.
 7. Themethod according to claim 6, wherein extracting the amplitude for afrequency comprises extracting an amplitude spectrum of the fast Fouriertransform of the calibrated vibration data.
 8. The method according toclaim 1, wherein the support vector machine classifier comprises aclassification function g taking a form of g(E)=wE−b, where E representsfeature vectors of the vibration data, w and b are parameters determinedby a machine learning process.
 9. The method according to claim 8,wherein the machine learning process comprises an unsupervised machinelearning process.
 10. A non-transitory computer-readable medium,including computer-executable instructions stored thereon that whenexecuted by a processor causes the processor to perform the method ofclaim
 1. 11. A system for determining a health condition of a part of astructure, the system comprising: two or more accelerometers providingvibration data, a processor configured to monitor a building structureby constantly or regularly performing the steps of: receiving oraccessing the vibration data of the part of the structure, the vibrationdata being measured by the two or more accelerometers, the vibrationdata representing acceleration of the part of the structuresubstantially caused by an external vibration source; transforming thevibration data by aligning an axis direction of the vibration data fromeach of the two or more accelerometers with a direction corresponding tomaximum variance of the vibration data to create calibrated vibrationdata; extracting an amplitude for a frequency of the calibratedvibration data based on frequency analysis of the calibrated vibrationdata; and determining the health condition of the part of the structureby a support vector machine classifier based on the amplitude for thefrequency.
 12. A computer-implemented method for training a supportvector machine classifier, the method comprising: monitoring a buildingstructure by constantly or regularly performing the steps of: receivingor accessing vibration data of one or more parts of one or morestructures measured by two or more accelerometers, the vibration datarepresenting acceleration of the part of the structure substantiallycaused by an external vibration source; transforming the vibration databy aligning an axis direction of the vibration data from each of the twoor more accelerometers with a direction corresponding to maximumvariance of the vibration data to create calibrated vibration data;extracting two or more amplitudes for a frequency of the calibratedvibration data based on frequency analysis of the calibrated vibrationdata; and applying a machine learning process to determine the supportvector machine classifier based on the two or more amplitudes for afrequency.
 13. A non-transitory computer-readable medium, includingcomputer-executable instructions, stored thereon that when executed by aprocessor, causes the processor to perform the method of claim
 12. 14. Asystem for training a support vector machine classifier for use indetermining a health condition of a part of a structure, the systemcomprising: two or more accelerometers providing vibration data of thepart of the structure, a processor configured to monitor a buildingstructure by constantly or regularly performing the steps of: receivingor accessing the vibration data of the part of the structure, thevibration data being measured by the two or more accelerometers, thevibration data representing acceleration of the part of the structuresubstantially caused by an external vibration source; transforming thevibration data by aligning an axis direction of the vibration data fromeach of the two or more accelerometers with a direction corresponding tomaximum variance of the vibration data to create calibrated vibrationdata; extracting two or more amplitudes for a frequency of thecalibrated vibration data based on frequency analysis of the calibratedvibration data; and applying a machine learning process to determine thesupport vector machine classifier based on the two or more amplitudesfor a frequency.